Devices utilizing visual interfaces (e.g., digital camera technology) to capture images of objects (e.g., living and nonliving) are now ubiquitous. And it is often desirable, for a variety of reasons, to differentiate between objects in captured images. In industrial applications, for example, it is often desirable for robotic equipment to select a particular object from among other objects so the particular object may be processed in some way.
As another example, in the context of devices providing users with augmented reality, it is often desirable for objects in an image to be differentiated from other objects so that a particular target object may be selected and the device display enhanced in a manner that is different from other objects in the image.
One technique that is utilized in existing systems to acquire a target object in captured images is to compare stored image data for a target object with the content of a captured image in an effort to differentiate the target object from other objects in the image. This type of comparison, however, is a complex and process-intensive task. In particular, target object differentiation in low contrast environments and/or cluttered environments (e.g., environments with many objects) is a particularly challenging task. And the difficulty of distinguishing between objects is increased even more when visually-similar objects are analyzed.
Accordingly, current object differentiation techniques are not always able to discriminate a target object from other objects, and will most certainly not be satisfactory in the future.